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Game Design Player Need Satisfaction Audit

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Stanestane
game-design-player-need-satisfaction-audit

Audit a game, feature, live-ops system, onboarding flow, progression loop, social feature, monetization flow, or return loop for player need satisfaction using Self-Determination Theory and the PENS lens. Use when evaluating whether a design is actually fun beyond surface KPIs, diagnosing weak retention or shallow engagement, comparing variants, identifying where a system denies autonomy, competence, or relatedness, or understanding whether a game feels emotionally nourishing or quietly depleting.

Overview

PublisherStanestane
Repositorygame-design-skills-bundle
Skill namegame-design-player-need-satisfaction-audit
Stars
87
Forks
18
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Stanestane on GitHub. Read the source before you install it.

Installation

Install the Game Design Player Need Satisfaction Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Stanestane/game-design-skills-bundle/tree/master/game-design-player-need-satisfaction-audit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Stanestane/game-design-skills-bundle.git /tmp/game-design-skills-bundle
mkdir -p .claude/skills
cp -r /tmp/game-design-skills-bundle/game-design-player-need-satisfaction-audit .claude/skills/game-design-player-need-satisfaction-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Design Player Need Satisfaction Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Game Design Player Need Satisfaction Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Game Design Player Need Satisfaction Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Game Design Player Need Satisfaction Audit

Audit a design by asking whether it satisfies core psychological needs rather than merely driving activity.

Use this skill to examine whether a game, feature, or live-ops system supports autonomy, competence, and relatedness, and where it may be denying those needs instead. Keep the analysis practical and design-facing. Treat fun as need satisfaction rather than as a vague entertainment label.

Core principle

People do not play games only because they are interactive or rewarding. They play because games satisfy core psychological needs. A feature can have solid metrics, clean progression, and monetization hooks, yet still feel emotionally weak if it fails to satisfy these needs.

Need lenses

1. Autonomy

The need to feel like a causal agent of one's own actions.

In game terms: meaningful choice, ownership, self-directed action, strategic freedom, and perceived control.

2. Competence

The need to feel effective, capable, and progressively more skillful.

In game terms: clear goals, understandable feedback, mastery, successful execution, improvement, and meaningful progress.

3. Relatedness

The need to feel socially connected, embedded, recognized, compared, valued, or bonded with others.

In game terms: cooperation, rivalry, status visibility, shared progress, belonging, social ritual, and social meaning.

4. Benevolence (optional)

Use this supplementary lens when the design includes helping, gifting, nurturing, stewardship, caretaking, or contribution to others.

What to produce

Generate an audit with these outputs:

  1. Need satisfaction profile - how strongly the design satisfies autonomy, competence, and relatedness
  2. Need denial profile - where the design frustrates or blocks those needs
  3. Mechanism map - which systems create or reduce satisfaction
  4. Risk diagnosis - where the design is emotionally hollow, coercive, or overly one-dimensional
  5. Improvement recommendations - targeted design changes to improve need satisfaction

Process

1. Define the audit target

Clarify exactly what is being audited.

Possible targets:

  • full game
  • core loop
  • new feature
  • onboarding
  • event structure
  • social system
  • monetization flow
  • return loop
  • session opener

Write:

  • Audit target
  • Player context
  • Why this audit matters

2. Map intended player experience

Describe what the design is supposed to make the player feel and do.

This step prevents theory from floating free of the actual experience.

Ask:

  • What is the intended player fantasy?
  • What actions define the loop?
  • What are players choosing?
  • What are they trying to master?
  • Where are they encountering other people, directly or indirectly?
  • What does success look like from the player's perspective?

Write:

  • Intended player experience
  • Core actions
  • Sources of progress
  • Sources of social meaning

3. Audit autonomy

Ask whether the design helps the player feel like an active agent rather than a passenger.

Signs of autonomy satisfaction:

  • meaningful choices, not fake choices
  • multiple valid paths or priorities
  • flexible pacing or self-directed goals
  • player expression of preference or playstyle
  • guidance that supports choice rather than replacing it
  • actions that feel intentional and consequential
  • constraints that are understandable rather than arbitrary

Signs of autonomy denial:

  • overly forced funnels
  • constant interruption or coercion
  • fake choice with one obviously correct option
  • rigid pacing with little ownership
  • recommendation systems that feel bossy
  • decisions that do not materially matter
  • excessive timers or blockers with no agency-preserving response

Ask:

  • What meaningful choices does the player make?
  • Can the player set their own short-term priorities?
  • Does guidance preserve agency or replace it?
  • Are there multiple viable ways to progress?
  • Does the player feel ownership over success?
  • Where does the system make the player feel trapped, railroaded, or manipulated?

Use this format:

Autonomy DimensionEvidence of SatisfactionEvidence of DenialSeverity

4. Audit competence

Ask whether the design helps the player feel effective, improving, and capable.

Signs of competence satisfaction:

  • clear goals and immediate feedback
  • understandable cause and effect
  • actions produce visible results
  • progression is legible
  • player skill or understanding improves outcomes
  • challenge is meaningful but not chaotic
  • the system teaches without humiliating

Signs of competence denial:

  • noisy or ambiguous feedback
  • success feels random or disconnected from decisions
  • actions fail for opaque reasons
  • no sense of improvement or mastery
  • friction overwhelms learning
  • players cannot tell what good play looks like
  • systems produce repeated confusion, blocked states, or dead-end effort

Ask:

  • Does the player understand what they are trying to achieve?
  • Is the result of action legible and satisfying?
  • Can the player improve through practice, strategy, or understanding?
  • Is there a clear bridge between effort and result?
  • Are failure states fair and interpretable?
  • Where does the design create frustration without learning?

Use this format:

Competence DimensionEvidence of SatisfactionEvidence of DenialSeverity

5. Audit relatedness

Ask whether the design makes the player feel socially connected, situated, or recognized.

Signs of relatedness satisfaction:

  • meaningful club, guild, or social systems
  • visible social comparison that feels motivating
  • cooperation, gifting, helping, or mutual dependency
  • rituals of return and shared participation
  • recognition, identity, or status within a group
  • ambient social presence, even if asynchronous
  • emotional connection to characters, community, or shared world

Signs of relatedness denial:

  • social systems are present but emotionally empty
  • comparison is punishing rather than connective
  • other players feel like abstract obstacles only
  • no sense of belonging or shared culture
  • social actions are transactional with no felt relationship
  • players feel isolated even inside a nominally social feature

Ask:

  • How does this design help the player feel part of a larger social matrix?
  • What forms of recognition or comparison exist?
  • Is there cooperation, competition, shared identity, or belonging?
  • Are social features emotionally meaningful or just functional?
  • Does the design create connection or merely visibility?
  • Where does the feature feel socially sterile?

Use this format:

Relatedness DimensionEvidence of SatisfactionEvidence of DenialSeverity

6. Audit benevolence or contribution when relevant

Use this supplementary lens when the design lets players feel good by helping, nurturing, protecting, or contributing.

Relevant cases include:

  • city care
  • co-op support
  • gifting
  • club contribution
  • stewardship systems
  • pet or resident care
  • restoration or rebuilding fantasies

Ask:

  • Can the player improve something beyond themselves?
  • Can they care for, help, or contribute to others?
  • Does contribution feel meaningful or cosmetic?
  • Is there emotional payoff in generosity or stewardship?

7. Build the need satisfaction profile

Summarize the overall shape of the experience.

Score each need from 1 to 5:

  • 1 = strongly denied
  • 2 = weak or inconsistent
  • 3 = moderate
  • 4 = strong
  • 5 = central strength

Use this format:

NeedScoreWhy
Autonomy1-5...
Competence1-5...
Relatedness1-5...
Benevolence (optional)1-5...

Interpretation patterns:

  • high autonomy, low competence -> expressive but confusing
  • high competence, low autonomy -> polished but controlling
  • high relatedness, low autonomy -> socially sticky but personally shallow
  • high competence, low relatedness -> satisfying alone, weak community pull
  • balanced strength across all three -> strongest candidate for durable engagement

8. Diagnose need denial and imbalance

Identify what is missing, what is overrepresented, and what failure shape is emerging.

Common failure shapes:

A. Spreadsheet fun
  • competence signals are present
  • autonomy is weak
  • relatedness is weak
  • the system is efficient but emotionally dry
B. Coercive retention
  • progression exists
  • constant nudging and timers deny autonomy
  • competence is undermined by blockers
  • players return from obligation rather than desire
C. Social shell
  • clubs or leaderboards exist
  • relatedness cues are visible but shallow
  • there is little real belonging or identity
D. Pleasant but empty
  • autonomy is present
  • low challenge or poor feedback reduces competence
  • interaction lacks a meaningful arc
E. High-pressure optimization trap
  • competence exists for experts only
  • autonomy narrows into one dominant strategy
  • social comparison becomes discouraging

9. Convert findings into design actions

For each issue, specify:

  • Need affected
  • Current problem
  • Design cause
  • Suggested change
  • Expected emotional effect

Examples:

  • add more player-selectable priorities -> increases autonomy
  • clarify action-result feedback -> increases competence
  • add visible club contribution loops -> increases relatedness
  • reduce forced interruptions -> reduces autonomy denial
  • add progression bridges between systems -> strengthens competence and autonomy together

Use this format:

NeedProblemSuggested ChangeExpected Effect

10. Reuse the audit over time

Run this audit:

  • during concepting
  • after prototype reviews
  • before greenlight
  • after live launch
  • when retention or sentiment drops
  • when comparing AB variants

Do not treat this as a one-off theory exercise. It is most useful when repeatedly applied to major game systems.

Response structure

Use this structure unless the user asks for something else:

Audit Target

  • ...

Intended Player Experience

  • ...

Autonomy

  • Strengths: ...
  • Weaknesses: ...

Competence

  • Strengths: ...
  • Weaknesses: ...

Relatedness

  • Strengths: ...
  • Weaknesses: ...

Need Satisfaction Profile

  • Autonomy: ...
  • Competence: ...
  • Relatedness: ...

Risk Diagnosis

  • ...

Recommendations

  1. ...
  2. ...
  3. ...

Fast mode

Use this quick pass when speed matters:

  • Where does the player experience meaningful choice?
  • Where do they feel effective and improving?
  • Where do they feel connected to others?
  • Where does the design deny one of those needs?
  • Which need is strongest, and which is weakest?
  • What one change would most improve the weakest need?

Usage notes

This audit is especially useful for:

  • session opener flows
  • city request systems
  • season passes and event track progression
  • club systems and wars
  • trains and deliveries
  • city-building fantasy versus optimization friction
  • monetization touchpoints that may erode autonomy
  • guidance systems that may help competence while harming autonomy

Common patterns to watch for:

  • too much instruction can improve competence but reduce autonomy
  • timers and blockers can motivate return but also create autonomy denial
  • social systems can create relatedness cues without true belonging
  • event layers can create progress but overload competence if feedback is fragmented

Working principle

A successful game does not merely retain players. It repeatedly satisfies core psychological needs.

Use this skill when a design is performing mechanically but you need to understand whether it is emotionally nourishing or quietly depleting.


Part of the Game Design Skills Bundle by Stanislav Stankovic. Licensed under PolyForm Noncommercial 1.0.0 — free for personal, academic and nonprofit use. Commercial use requires a licence, sold as skill packs. See https://github.com/Stanestane/game-design-skills-bundle for terms.

Required Notice: Copyright Stanislav Stankovic (https://www.stane-island.net/)

Frequently asked questions

What does the Game Design Player Need Satisfaction Audit AI skill do?

Audit a game, feature, live-ops system, onboarding flow, progression loop, social feature, monetization flow, or return loop for player need satisfaction using Self-Determination Theory and the PENS lens. Use when evaluating whether a design is actually fun beyond surface KPIs, diagnosing weak retention or shallow engagement, comparing variants, identifying where a system denies autonomy, competence, or relatedness, or understanding whether a game feels emotionally nourishing or quietly depleting.

Why use Game Design Player Need Satisfaction Audit on TypingMind?

Because you install it once and use it with any model. Game Design Player Need Satisfaction Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Game Design Player Need Satisfaction Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Stanestane/game-design-skills-bundle/tree/master/game-design-player-need-satisfaction-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Game Design Player Need Satisfaction Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Game Design Player Need Satisfaction Audit?

As many as you like. As long as a model supports skills, you can use Game Design Player Need Satisfaction Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Game Design Player Need Satisfaction Audit AI skill free?

It is published on GitHub by Stanestane. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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